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A homothetic data generated technology

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  • Peyrache, Antonio

Abstract

I propose a method for constructing an enlargement of a variable returns to scale production technology that will satisfy homotheticity. The method can be used both with DEA and FDH single output (or single input) technologies and it is computationally fast. The method is constructed by adding a restriction to the axiomatically delineated homothetic reference technologies which requires these reference technologies to be subsets of the minimal reference technology that satisfies constant returns to scale. Within this set it is possible to identify a homothetic technology that satisfies the property of minimum extrapolation.

Suggested Citation

  • Peyrache, Antonio, 2024. "A homothetic data generated technology," European Journal of Operational Research, Elsevier, vol. 316(1), pages 255-267.
  • Handle: RePEc:eee:ejores:v:316:y:2024:i:1:p:255-267
    DOI: 10.1016/j.ejor.2024.01.031
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    References listed on IDEAS

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    1. Kerstens, Kristiaan & Vanden Eeckaut, Philippe, 1999. "Estimating returns to scale using non-parametric deterministic technologies: A new method based on goodness-of-fit," European Journal of Operational Research, Elsevier, vol. 113(1), pages 206-214, February.
    2. R. Russell & William Schworm, 2009. "Axiomatic foundations of efficiency measurement on data-generated technologies," Journal of Productivity Analysis, Springer, vol. 31(2), pages 77-86, April.
    3. Walter Briec & Kristiaan Kerstens, 2006. "Input, output and graph technical efficiency measures on non-convex FDH models with various scaling laws: An integrated approach based upon implicit enumeration algorithms," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 14(1), pages 135-166, June.
    4. Podinovski, V. V., 2004. "On the linearisation of reference technologies for testing returns to scale in FDH models," European Journal of Operational Research, Elsevier, vol. 152(3), pages 800-802, February.
    5. Antonio Peyrache, 2013. "Multilateral productivity comparisons and homotheticity," Journal of Productivity Analysis, Springer, vol. 40(1), pages 57-65, August.
    6. Podinovski, Victor V. & Bouzdine-Chameeva, Tatiana, 2019. "Cone extensions of polyhedral production technologies," European Journal of Operational Research, Elsevier, vol. 276(2), pages 736-743.
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    9. J.H. Dulá & R.M. Thrall, 2001. "A Computational Framework for Accelerating DEA," Journal of Productivity Analysis, Springer, vol. 16(1), pages 63-78, July.
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    11. Hanoch, Giora & Rothschild, Michael, 1972. "Testing the Assumptions of Production Theory: A Nonparametric Approach," Journal of Political Economy, University of Chicago Press, vol. 80(2), pages 256-275, March-Apr.
    12. Timothy J. Coelli & D.S. Prasada Rao & Christopher J. O’Donnell & George E. Battese, 2005. "An Introduction to Efficiency and Productivity Analysis," Springer Books, Springer, edition 0, number 978-0-387-25895-9, December.
    13. Olesen, Ole B., 2014. "A homothetic reference technology in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 233(3), pages 759-771.
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    Cited by:

    1. Antonio Peyrache, 2024. "Homothetic Data Generated Production Metatechnologies," CEPA Working Papers Series WP022024, School of Economics, University of Queensland, Australia.

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